Secure and Cost-Efficient Microservice Placement in MEC Through Network-Aware Adaptation

Mobile Edge Computing (MEC) facilitates low-latency service delivery by bringing computation to the network edge, whilemicroservice architectures enhance system lexibility and scalability through modular application decomposition. Theirintegration allows applications to dynamically scale and eiciently isolate faults in response to luctuating demand. However,the heterogeneous and dynamic nature of edge environments poses signiicant challenges to the cost eiciency, security, andprivacy of such deployments. Unlike prior work that handle security and privacy as constraints or overlook AN dynamics, wecharacterize both AN selection and service placement as mutually dependent decision layers within a uniied control structure.Our proposal balances the computational overheads incurred by security and privacy veriication against deployment costs,preventing aggressive cost-reduction eforts from compromising system protections. Considering the unpredictability ofrun-time and network conditions, we propose an online algorithm for progressive decision-making. The deployment problemis decomposed into one-slot optimization sub-problems, each NP-hard, for which we develop a greedy-based heuristicalgorithm. Experiments across diverse loads, request patterns, and security/privacy thresholds show that the proposedalgorithm consistently achieves the lowest average deployment cost, with cost reductions of at least 41.67% compared with thebaselines, while maintaining comparable or higher request acceptance ratios and generally lower, well-balanced edge-serverworkloads